Recently, the number of internet-connected devices has been exponentially growing with great potential utilization in myriad applications, such as Intelligent Transportation Systems (ITS) [1]}, smart grids [2]}, smart healthcare [3]}, and smart industry [4]}. It is followed by the massive volume of heterogeneous data generated from various devices, resulting in a highly intertwined cyber-physical system. In terms of ITS, the concept of edge intelligence [5]} system (EIS), which leverages the merits of Mobile Edge Computing (MEC) and artificial intelligence (AI) technology, has been widely deployed to form the next generation of vehicular networks (VNs). MEC offers real-time communications with high bandwidth and low latency by locating the computing and processing infrastructure close to the end-user in the edge network. On the other hand, AI provides smart cloud services with high performance and reduces decision-making delays [6]}. Thus, EIS is designed to manage intelligent resource orchestration, enable self-aggregating communication systems, offer powerful computational processing, and reduce decision-making delay by leveraging edge resources on local edge networks [7]}.
